Date: Wed, 20 Dec 2006 11:16:16 -0600
Reply-To: Robin High <robinh@UNLSERVE.UNL.EDU>
Sender: "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From: Robin High <robinh@UNLSERVE.UNL.EDU>
Subject: Re: proc mixed for 2 groups (Placebo and all Active Treatment
Content-Type: TEXT/PLAIN; charset=US-ASCII
> Previous I asked this group for a proc mixed solution for Placebo vs.
> Multiple Treatment Groups and a Dunnett Test,
> and the answer was correct. I'm not a statistician (programmer). The
> solution was:
> proc mixed data=rx_1;
> by week;
> class center_eff treatment;
> model rx_change_rx = rx_baseline center_eff treatment / solution;
> lsmeans treatment / diff=control cl adjust=dunnett;
> Can this be setup for a T-Test for the 2 Treatment Groups Placebo vs.
> Active Groups (i.e. combined)
proc mixed data=rx_1;
class center_eff treatment;
model rx_change_rx = rx_baseline center_eff treatment / solution cl;
lsmeans treatment / diff cl;
If treatment has two groups (active and placebo) and you want to compare
two means like a t-test, then the LSMEANS statement as above will do it.
Also, the "solution" pvalue for treatment and the type 3 table from the
MODEL statement will also give you the same results. And while we're at
ESTIMATE 'active vs placebo' treatment 1 -1 / cl;
will also compute it.
Other items of interest is the name of the term center_eff -- is that
factor actually considered fixed, or could it be entered as a random
effect on a RANDOM statement?
RANDOM center_eff ;
(and thus removed from the MODEL statement).
And why is
entered? My next question, is there repeated measures on the same
subjects over time (unless, of course, you have different subjects each
week, then ignore this Q.).